This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The Genome Projects worldwide are rapidly pouring a wealth of DNA sequence data into databases at the National Institutes of Health (NIH) and many other repositories. Within this vast quantity of data lie the largely not-yet-understood blueprints which the individual cells in an organism use to build the array of proteins that serve as the molecular machines for executing the wide variety of biological processes necessary to sustain life. These ever-growing genomic databases serve as a fundamental resource in accelerating research using mass spectrometry for identification of proteins. Mass spectrometry techniques are the most powerful approaches for the characterization of proteins and peptides present in complex mixtures or after protein purification. The most common approach employed for protein identification is to use enzymes to digest proteins into peptides, fragment the peptides in the mass spectrometer and then use database searching software to match the observed peptides to those predicted from sequences in protein databases. However, recent instrument developments now make fragmentation analysis of intact proteins a practical approach. We develop searching software to assist researchers in interpreting both peptide-level and protein-level fragmentation analysis. In addition to identifying proteins, these approaches also have the ability to identify modifications attached to the protein that are used by the cell to regulate protein activity and localization. Experiments can also be designed to compare levels of peptide, modification and protein between related samples to report changes in quantity upon, for example, stimulation. Our software tools are also able to extract these elements of information from datasets. (Additional effort and instrument time reported under Collaborative projects and other Technical Research and Development projects.)